Section 18.1-2. in the Next 2-3 Lectures We Will Have a Lightning Introduction to Representations of finite Groups

Total Page:16

File Type:pdf, Size:1020Kb

Section 18.1-2. in the Next 2-3 Lectures We Will Have a Lightning Introduction to Representations of finite Groups Section 18.1-2. In the next 2-3 lectures we will have a lightning introduction to representations of finite groups. For any vector space V over a field F , denote by L(V ) the algebra of all linear operators on V , and by GL(V ) the group of invertible linear operators. Note that if dim(V ) = n, then L(V ) = Matn(F ) is isomorphic to the algebra of n×n matrices over F , and GL(V ) = GLn(F ) to its multiplicative group. Definition 1. A linear representation of a set X in a vector space V is a map φ: X ! L(V ), where L(V ) is the set of linear operators on V , the space V is called the space of the rep- resentation. We will often denote the representation of X by (V; φ) or simply by V if the homomorphism φ is clear from the context. If X has any additional structures, we require that the map φ is a homomorphism. For example, a linear representation of a group G is a homomorphism φ: G ! GL(V ). Definition 2. A morphism of representations φ: X ! L(V ) and : X ! L(U) is a linear map T : V ! U, such that (x) ◦ T = T ◦ φ(x) for all x 2 X. In other words, T makes the following diagram commutative φ(x) V / V T T (x) U / U An invertible morphism of two representation is called an isomorphism, and two representa- tions are called isomorphic (or equivalent) if there exists an isomorphism between them. Example. (1) A representation of a one-element set in a vector space V is simply a linear operator on V . Two such representations are isomorphic if two operators have the same Jordan Normal Form. (2) For any vector space, identity maps id: GL(V ) ! GL(V ) and id: L(V ) ! L(V ) give tautological representations of the group GL(V ) and the algebra L(V ) respectively. (3) For each representation φ: X ! L(V ) one can define its dual representation φ∗ : X ! L(V ∗); φ∗(x)(α)(v) = α(φ(x)(v)) for all x 2 X, v 2 V , and α 2 V ∗. (4) The trivial representation of a group G is a homomorphism φ: G ! GL(V ), where V is a 1-dimensional vector space over the field F , and φ(g) = 1 2 GL(V ) = F × for all g 2 G. n 2 −1 (5) Recall the dihedral group Dn = r; s j r = s = 1; rs = sr . The following map cos (2π=n) − sin (2π=n) 0 1 r 7! ; s 7! sin (2π=n) cos (2π=n) 1 0 defines a 2-dimensional representation φ: Dn ! GL(2; R). 1 2 (6) The following map gives a representation of a symmetric group S4: 00 1 0 01 01 0 0 01 01 0 0 01 B1 0 0 0C B0 0 1 0C B0 1 0 0C (12) 7! B C ; (23) 7! B C ; (34) 7! B C : @0 0 1 0A @0 1 0 0A @0 0 0 1A 0 0 0 1 0 0 0 1 0 0 1 0 (7) There is a natural representations of Sn on the space of polynomials in n variables: (σf)(x1; : : : ; xn) = f(xσ(1); : : : ; xσ(n)): Note, that symmetric polynomials stay invariant under this action. (8) Any Galois group Gal(K=F ) comes with its representation in K over the field F . Definition 3. An invariant subspace of a representation φ: X ! L(V ) is a subspace W ⊂ V invariant under φ(x) for all x 2 X. An invariant subspace gives rise to a subrepresentation φW : X ! L(W ); φW (x) = φ(x)jW ; and a factor representation φV=W : X ! L(V=W ); φV=W (x)(v + W ) = φ(x)(v): Remark 4. If one chooses a basis of the space V of a representation φ: X ! L(V ) in such a way that the first k vectors span an invariant subspace W ⊂ V , then any operator φ(x) is written in matrix form as φ (x) ∗ φ(x) = W 0 φV=W (x) Example. n (1) Let Sn act on F by permuting basis vectors, as in example (4) above. Then the subspace fa; a; : : : ; a) j a 2 F g ⊂ F n is a 1-dimensional invariant subspace. There is a complementary invariant subspace of dimension n − 1 defined by f(a1; : : : ; an) j a1 + ··· + an = 0g : Note that F n is isomorphic to the direct sum of these two subspaces. d (2) For any positive integer d, the subspace Poln of polynomials in n variables of degree d in the space Poln of all polynomials of n variables gives a subrepresentation of Sn. Definition 5. (1) A representation is irreducible (or simple) if it (or rather the underlying vector space) has no invariant subspaces other than 0 and itself, otherwise it is reducible. (2) A representation is indecomposable if it can not be written as a direct sum of two invariant subrepresentations. (3) A representation is completely reducible if it can be written as a direct sum of irre- ducible representations. Example. The representation of a 1-element set given by a 2-dimensional space V = F 2 and an operator a b A = 0 c is reducible, since the subspace he1i ⊂ V is invariant, however it is indecomposable unless b = 0. If b = 0, the representation V is completly reducible: V = he1i ⊕ he2i. Exercise 6. If T : V ! U is a morphism of representations of X, then ker(T ) ⊂ V and im(T ) ⊂ U are invariant subspaces. 3 Corollary 7. If V and U are irreducible representations of X, any morphism T : V ! U is either 0 or an isomorphism. Proof. Since V is irreducible, the subrepresentation ker(T ) is either 0, in which case T is injective, or coincides with the whole V , in which case T = 0. Since U is irreducible, we must now have im(T ) = U unless T = 0, which forces T to be an isomorphism. Corollary 8 (Schur's lemma). Any endomorphism T : V ! V of an irreducible represen- tation V of X over an algebraically closed field is a scalar, that is T = λ · Id for some λ 2 F . Proof. Let T 2 EndX (V ) = HomX (V; V ) be an endomorphism of V . Then for any λ 2 F , so is T −λ·Id. Choosing λ to be an eigenvalue of T (which we can always do over an algebraically closed field) we see that ker(T − λ · Id) is a subrepresentation of X in V . Therefore, since V is irreducible, we must have T = λ · Id. Corollary 9. Let φ: X ! L(V ) and : X ! L(U) be a pair of irreducible representations over an algebraically closed field. Then any morphisms T;S : V ! U differ by a scalar factor. Proof. If one of the two morphisms is zero, the statement is obvious. Otherwise, both mor- phisms are isomorphisms, hence ST −1 is an endomorphism of an irreducible representation −1 U which implies ST = λ Id. Corollary 10. Any irreducible representation of an abelian group over an algebraically closed field is 1-dimensional. Proof. If G is abelian and φ: G ! GL(V ) is a representation of G, operators φ(g) commute for all g 2 G, hence any of them can be viewed as an endomorphism of V . If V is irre- ducible, By Schur's lemma, all of them must be scalar, therefore any subspace is invariant and representation V can be irreducible if and only if dim(V ) = 1. 2 Example. The 2-dimensional plane R is an irreducible representation of the abelian group SO(2; R) of plane rotations cos θ − sin θ G = θ 2 R : sin θ cos θ However, over the field of complex numbers, we see that cos θ − sin θ eiθ 0 ∼ : sin θ cos θ 0 e−iθ Proposition 11. Every subrepresentation and factor representation of a completely reducible representation is completely reducible. Proof. Let V be a completely reducible representation, and U be its subrepresentation. 0 0 Then for any U1 ⊂ U, there exists an invarinat subspace V ⊂ V such that V ' U1 ⊕ V . 0 Setting U2 = V \ U we see that U2 is invariant and U ' U1 ⊕ U2. Now, let π : V ! V=W be a canonical projection to a factor representation, and U1 ⊂ V=W −1 be the subrepresentation. Then V1 = π (U1) is an invariant subspace of V , which con- tains W . Now, we have V = V1 ⊕ V2 for some invariant subspace V2, which implies that V=W ' U1 ⊕ U2 where U2 = π(V2) is an invariant subspace in V=W . Proposition 12. If a representation φ: X ! L(V ) is completely reducible, then V can be written as a direct sum of minimal (nonzero) irreducible subspaces. Conversely, if V can be written as a sum of minimal invariant subspaces V = V1 + ··· + Vn, then V is completely reducible. 4 Proof. The first statement is immediate: choose any minimal invariant subspace V1 ⊂ V and write V = V1 ⊕V2, continue the same process for V2. Now, let V = V1 +···+Vn for some minimal invariant subspaces Vi ⊂ V , and U ⊂ V be any invariant subspace. Then U \ Vj is either 0 or coincides with Vj for any j = 1; : : : ; n. Setting 0 X U = Vj j:U\Vj =? 0 we get V = U ⊕ U . Definition 13. A sum of representations φ: X ! L(V ) and : X ! L(U) is a representa- tion φ + : X ! V ⊕ U; (φ + )(x) = (φ(x); (x)): Remark 14.
Recommended publications
  • Solution to Homework 5
    Solution to Homework 5 Sec. 5.4 1 0 2. (e) No. Note that A = is in W , but 0 2 0 1 1 0 0 2 T (A) = = 1 0 0 2 1 0 is not in W . Hence, W is not a T -invariant subspace of V . 3. Note that T : V ! V is a linear operator on V . To check that W is a T -invariant subspace of V , we need to know if T (w) 2 W for any w 2 W . (a) Since we have T (0) = 0 2 f0g and T (v) 2 V; so both of f0g and V to be T -invariant subspaces of V . (b) Note that 0 2 N(T ). For any u 2 N(T ), we have T (u) = 0 2 N(T ): Hence, N(T ) is a T -invariant subspace of V . For any v 2 R(T ), as R(T ) ⊂ V , we have v 2 V . So, by definition, T (v) 2 R(T ): Hence, R(T ) is also a T -invariant subspace of V . (c) Note that for any v 2 Eλ, λv is a scalar multiple of v, so λv 2 Eλ as Eλ is a subspace. So we have T (v) = λv 2 Eλ: Hence, Eλ is a T -invariant subspace of V . 4. For any w in W , we know that T (w) is in W as W is a T -invariant subspace of V . Then, by induction, we know that T k(w) is also in W for any k. k Suppose g(T ) = akT + ··· + a1T + a0, we have k g(T )(w) = akT (w) + ··· + a1T (w) + a0(w) 2 W because it is just a linear combination of elements in W .
    [Show full text]
  • Spectral Coupling for Hermitian Matrices
    Spectral coupling for Hermitian matrices Franc¸oise Chatelin and M. Monserrat Rincon-Camacho Technical Report TR/PA/16/240 Publications of the Parallel Algorithms Team http://www.cerfacs.fr/algor/publications/ SPECTRAL COUPLING FOR HERMITIAN MATRICES FRANC¸OISE CHATELIN (1);(2) AND M. MONSERRAT RINCON-CAMACHO (1) Cerfacs Tech. Rep. TR/PA/16/240 Abstract. The report presents the information processing that can be performed by a general hermitian matrix when two of its distinct eigenvalues are coupled, such as λ < λ0, instead of λ+λ0 considering only one eigenvalue as traditional spectral theory does. Setting a = 2 = 0 and λ0 λ 6 e = 2− > 0, the information is delivered in geometric form, both metric and trigonometric, associated with various right-angled triangles exhibiting optimality properties quantified as ratios or product of a and e. The potential optimisation has a triple nature which offers two j j e possibilities: in the case λλ0 > 0 they are characterised by a and a e and in the case λλ0 < 0 a j j j j by j j and a e. This nature is revealed by a key generalisation to indefinite matrices over R or e j j C of Gustafson's operator trigonometry. Keywords: Spectral coupling, indefinite symmetric or hermitian matrix, spectral plane, invariant plane, catchvector, antieigenvector, midvector, local optimisation, Euler equation, balance equation, torus in 3D, angle between complex lines. 1. Spectral coupling 1.1. Introduction. In the work we present below, we focus our attention on the coupling of any two distinct real eigenvalues λ < λ0 of a general hermitian or symmetric matrix A, a coupling called spectral coupling.
    [Show full text]
  • Adiabatic Invariants of the Linear Hamiltonian Systems with Periodic Coefficients
    JOURNAL OF DIFFERENTIAL EQUATIONS 42, 47-71 (1981) Adiabatic Invariants of the Linear Hamiltonian Systems with Periodic Coefficients MARK LEVI Department of Mathematics, Duke University, Durham, North Carolina 27706 Received November 19, 1980 0. HISTORICAL REMARKS In 1911 Lorentz and Einstein had offered an explanation of the fact that the ratio of energy to the frequency of radiation of an atom remains constant. During the long time intervals separating two quantum jumps an atom is exposed to varying surrounding electromagnetic fields which should supposedly change the ratio. Their explanation was based on the fact that the surrounding field varies extremely slowly with respect to the frequency of oscillations of the atom. The idea can be illstrated by a slightly simpler model-a pendulum (instead of Bohr’s model of an atom) with a slowly changing length (slowly with respect to the frequency of oscillations of the pendulum). As it turns out, the ratio of energy of the pendulum to its frequency changes very little if its length varies slowly enough from one constant value to another; the pendulum “remembers” the ratio, and the slower the change the better the memory. I had learned this interesting historical remark from Wasow [15]. Those parameters of a system which remain nearly constant during a slow change of a system were named by the physicists the adiabatic invariants, the word “adiabatic” indicating that one parameter changes slowly with respect to another-e.g., the length of the pendulum with respect to the phaze of oscillations. Precise definition of the concept will be given in Section 1.
    [Show full text]
  • A Criterion for the Existence of Common Invariant Subspaces Of
    Linear Algebra and its Applications 322 (2001) 51–59 www.elsevier.com/locate/laa A criterion for the existence of common invariant subspaces of matricesୋ Michael Tsatsomeros∗ Department of Mathematics and Statistics, University of Regina, Regina, Saskatchewan, Canada S4S 0A2 Received 4 May 1999; accepted 22 June 2000 Submitted by R.A. Brualdi Abstract It is shown that square matrices A and B have a common invariant subspace W of di- mension k > 1 if and only if for some scalar s, A + sI and B + sI are invertible and their kth compounds have a common eigenvector, which is a Grassmann representative for W.The applicability of this criterion and its ability to yield a basis for the common invariant subspace are investigated. © 2001 Elsevier Science Inc. All rights reserved. AMS classification: 15A75; 47A15 Keywords: Invariant subspace; Compound matrix; Decomposable vector; Grassmann space 1. Introduction The lattice of invariant subspaces of a matrix is a well-studied concept with sev- eral applications. In particular, there is substantial information known about matrices having the same invariant subspaces, or having chains of common invariant subspac- es (see [7]). The question of existence of a non-trivial invariant subspace common to two or more matrices is also of interest. It arises, among other places, in the study of irreducibility of decomposable positive linear maps on operator algebras [3]. It also comes up in what might be called Burnside’s theorem, namely that if two square ୋ Research partially supported by NSERC research grant. ∗ Tel.: +306-585-4771; fax: +306-585-4020. E-mail address: [email protected] (M.
    [Show full text]
  • The Semi-Simplicity Manifold of Arbitrary Operators
    THE SEMI-SIMPLICITY MANIFOLD OF ARBITRARY OPERATORS BY SHMUEL KANTOROVITZ(i) Introduction. In finite dimensional complex Euclidian space, any linear operator has a unique maximal invariant subspace on which it is semi-simple. It is easily described by means of the Jordan decomposition theorem. The main result of this paper (Theorem 2.1) is a generalization of this fact to infinite di- mensional reflexive Banach space, for arbitrary bounded operators T with real spectrum. Our description of the "semi-simplicity manifold" for T is entirely analytic and basis-free, and seems therefore to be a quite natural candidate for such generalizations to infinite dimension. A similar generalization to infinite dimension of the concepts of Jordan cells and Weyr characteristic will be presented elsewhere. The theory is motivated and illustrated by examples in §3. Notations. The following notations are fixed throughout this paper, and will be used without further explanation. R: the field of real numbers. C : the field of complex numbers. âd : the sigma-algebra of all Borel subsets of R. C(R) : the space of all continuous complex valued functions on R. L"(R) : the usual Lebesgue spaces on R (p = 1). j : integration over R. /: the Fourier transform of / e L1(R). X : an arbitrary complex Banach space. X*: the conjugate of X. B(X) : the Banach algebra of all bounded linear operators acting on X. I : the identity operator on X. | • | : the original norms on X, X* and B(X) (new norms, to be defined, will be denoted by double bars). | • \y, | • |œ: the L\R) andL™^) norms (respectively).
    [Show full text]
  • The Common Invariant Subspace Problem
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Linear Algebra and its Applications 384 (2004) 1–7 www.elsevier.com/locate/laa The common invariant subspace problem: an approach via Gröbner bases Donu Arapura a, Chris Peterson b,∗ aDepartment of Mathematics, Purdue University, West Lafayette, IN 47907, USA bDepartment of Mathematics, Colorado State University, Fort Collins, CO 80523, USA Received 24 March 2003; accepted 17 November 2003 Submitted by R.A. Brualdi Abstract Let A be an n × n matrix. It is a relatively simple process to construct a homogeneous ideal (generated by quadrics) whose associated projective variety parametrizes the one-dimensional invariant subspaces of A. Given a finite collection of n × n matrices, one can similarly con- struct a homogeneous ideal (again generated by quadrics) whose associated projective variety parametrizes the one-dimensional subspaces which are invariant subspaces for every member of the collection. Gröbner basis techniques then provide a finite, rational algorithm to deter- mine how many points are on this variety. In other words, a finite, rational algorithm is given to determine both the existence and quantity of common one-dimensional invariant subspaces to a set of matrices. This is then extended, for each d, to an algorithm to determine both the existence and quantity of common d-dimensional invariant subspaces to a set of matrices. © 2004 Published by Elsevier Inc. Keywords: Eigenvector; Invariant subspace; Grassmann variety; Gröbner basis; Algorithm 1. Introduction Let A and B be two n × n matrices. In 1984, Dan Shemesh gave a very nice procedure for constructing the maximal A, B invariant subspace, N,onwhichA and B commute.
    [Show full text]
  • Some Open Problems in the Theory of Subnormal Operators
    Holomorphic Spaces MSRI Publications Volume 33, 1998 Some Open Problems in the Theory of Subnormal Operators JOHN B. CONWAY AND LIMING YANG Abstract. Subnormal operators arise naturally in complex function the- ory, differential geometry, potential theory, and approximation theory, and their study has rich applications in many areas of applied sciences as well as in pure mathematics. We discuss here some research problems concern- ing the structure of such operators: subnormal operators with finite-rank self-commutator, connections with quadrature domains, invariant subspace structure, and some approximation problems related to the theory. We also present some possible approaches for the solution of these problems. Introduction A bounded linear operator S on a separable Hilbert space H is called subnor- mal if there exists a normal operator N on a Hilbert space K containing H such that NH ⊂ H and NjH = S. The operator S is called cyclic if there exists an x in H such that H = closfp(S)x : p is a polynomialg; and is called rationally cyclic if there exists an x such that H = closfr(S)x : r is a rational function with poles off σ(S)g: The operator S is pure if S has no normal summand and is irreducible if S is not unitarily equivalent to a direct sum of two nonzero operators. The theory of subnormal operators provides rich applications in many areas, since many natural operators that arise in complex function theory, differential geometry, potential theory, and approximation theory are subnormal operators. Many deep results have been obtained since Halmos introduced the concept of a subnormal operator.
    [Show full text]
  • Representations
    Representations 1 Basic definitions If V is a k-vector space, we denote by Aut V the group of k-linear iso- morphisms F : V ! V and by End V the k-vector space of k-linear maps n ∼ ∼ F : V ! V . Thus, if V = k , then Aut V = GL(n; k) and End V = Mn(k). In general, if V is a finite dimensional vector space of dimension n, then a choice of basis defines a group isomorphism Aut V =∼ GL(n; k) and a vector ∼ space End V = Mn(k). From now on, unless otherwise stated, k = C, i.e all vector spaces are C-vector spaces, all linear maps are C-linear, and all vector subspaces are closed under scalar multiplication by C. Definition 1.1. Let G be a group. A representation of G on V is a homomorphism ρ: G ! Aut V , where V is a finite dimensional C-vector space. Equivalently, for all g 2 G, ρ(g): V ! V is a linear map satisfying: ρ(g)(ρ(h)(v)) = ρ(gh)(v) and ρ(1) = Id, i.e. a representation is equivalent to an action of G on V by linear maps. The degree deg ρ is by definition dim V . Finally, a choice of basis of V identifies ρ with a homomorphism (also denoted ρ) from G to GL(n; C). Changing the basis replaces ρ by T ρT −1, where T is an invertible matrix. We will usually abbreviate the data of the representation ρ: G ! Aut V by ρ, or frequently by V , with the understanding that the vector space V includes the data of the homomorphism ρ or of the G-action.
    [Show full text]
  • Discrete Symmetries of Dynamics 869
    APPENDIX A7. DISCRETE SYMMETRIES OF DYNAMICS 869 1. If g , g G, then g g G. 1 2 ∈ 2 ◦ 1 ∈ 2. The group multiplication is associative: g (g g ) = (g g ) g . 3 ◦ 2 ◦ 1 3 ◦ 2 ◦ 1 3. The group G contains identity element e such that g e = e g = g for every ◦ ◦ element g G. ∈ 4. For every element g G, there exists a unique h == g 1 G such that Appendix A7 ∈ − ∈ h g = g h = e. ◦ ◦ A finite group is a group with a finite number of elements Discrete symmetries of dynamics G = e, g2,..., g G , { | |} where G , the number of elements, is the order of the group. | | Example A7.1 Finite groups: Some finite groups that frequently arise in asic group-theoretic notions are recapitulated here: groups, irreducible rep- applications: resentations, invariants. Our notation follows birdtracks.eu. Cn (also denoted Zn): the cyclic group of order n. B • Dn: the dihedral group of order 2n, rotations and reflections in plane that preserve The key result is the construction of projection operators from invariant ma- • a regular n-gon. trices. The basic idea is simple: a hermitian matrix can be diagonalized. If this S n: the symmetric group of all permutations of n symbols, order n!. matrix is an invariant matrix, it decomposes the reps of the group into direct sums • of lower-dimensional reps. Most of computations to follow implement the spectral decomposition Example A7.2 Cyclic and dihedral groups: The cyclic group Cn SO(2) of order ⊂ M = λ P + λ P + + λrPr , n is generated by one element.
    [Show full text]
  • ON the INVARIANT SUBSPACE PROBLEM 1. Introduction
    ON THE INVARIANT SUBSPACE PROBLEM M. SABABHEH1, A. YOUSEF2 AND R. KHALIL3 Abstract. In an attempt to solve the Invariant Subspace Problem, we intro- duce a certain orthonormal basis of Hilbert spaces, and prove that a bounded linear operator on a Hilbert space must have an invariant subspace once this basis fulfills certain conditions. Ultimately, this basis is used to show that ev- ery bounded linear operator on a Hilbert space is the sum of a shift and an upper triangular operators, each of which having an invariant subspace. 1. Introduction Let H be a complex Hilbert space, and L(H; H) be the space of all bounded linear operators acting on H. A closed subspace W of H is called invariant or T - invariant, of T 2 L(H; H), if T (W ) ⊂ W . The invariant subspace problem is the long standing simple question:"Does every bounded linear operator T 2 L(H; H) have a non-trivial invariant subspace?". Non-trivial subspace means a closed subspace different from f0g and H. This problem is open for more than half a century, and mathematicians have been trying to attack this problem producing many significant contributions with a huge variety of techniques, making this challenging problem a target. However the solution seems to be nowhere in sight. It is unknown who first posed this question. It appeared in 1949-1950, years after Von Neumann's unpublished result (1930's) , in which he proved that every compact operator T on a Hilbert space has nontrivial invariant subspace. In this paper we shall discuss, briefly, the most popular known results, and introduce our new technique toward solving this problem.
    [Show full text]
  • 5. Solutions to Exercise 5 Exercise 5.1. Suppose V Is Finite-Dimensional. Then Each Operator on V Has at Most Dim V Distinct E
    24 5. Solutions to Exercise 5 Exercise 5.1. Suppose V is finite-dimensional. Then each operator on V has at most dim V distinct eigenvalues. Solution 5.1. Let T 2 L(V ). Suppose λ1; : : : ; λm are distinct eigenvalues of T . Let v1; : : : ; vm be corresponding eigenvectors. Then fv1; : : : ; vmg is linearly independent. Thus m ≤ dim V . Exercise 5.2. (1) Suppose S; T 2 L(V ) are such that ST = TS. Prove that Nul(S) is invariant under T . Solution 5.2. For any v 2 Nul(S), S(v) = 0. Since ST = TS, S(T (v)) = T (S(v)) = T (0) = 0. Then T (v) 2 Nul(S). Then Nul(S) is invariant under T . (2) Suppose S; T 2 L(V ) are such that ST = TS. Prove that im(S) is invariant under T . Solution 5.2. For any v 2 im(S), there exists w 2 V such that S(w) = v. Since ST = TS, T (v) = T (S(w)) = S(T (w)). Then T (v) 2 im(S). Then im(S) is invariant under T . Exercise 5.3. See the proof of Theorem 5.4.1. Let v 2 V . Let f[v1];:::; [vk]g be a basis V / Span(v). Please show that fv; v1; : : : ; vkg is linearly independent using the definition of linearly independence. Solution 5.3. Set up the equation in V : cv + c1v1 + ::: + ckvk = 0: (**) Then c1v1 + ::: + ckvk = −cv 2 Span(v). Then [c1v1 + ::: + ckvk] = 0 2 V / Span(v). So c1[v1] + ::: + ck[vk] = [c1v1 + ::: + ckvk] = 0: Since f[v1];:::; [vk]g is a basis of V / Span(v), c1 = ::: = ck = 0.
    [Show full text]
  • Symmetric and Hermitian Matrices : a Geometric Perspective on Spectral Coupling
    Symmetric and hermitian matrices : a geometric perspective on spectral coupling Franc¸oise Chatelin and M. Monserrat Rincon-Camacho Technical Report TR/PA/15/56 Publications of the Parallel Algorithms Team http://www.cerfacs.fr/algor/publications/ SYMMETRIC AND HERMITIAN MATRICES : A GEOMETRIC PERSPECTIVE ON SPECTRAL COUPLING CERFACS REPORT TR/PA/15/56 FRANC¸OISE CHATELIN (1);(2) AND M. MONSERRAT RINCON-CAMACHO (1) Abstract. The report develops the presentation begun in [Chatelin and Rincon-Camacho, 2015], of the information processing that can be performed by a general hermitian matrix of order n when two of its distinct eigenvalues are coupled, such 0 λ+λ0 λ0−λ as λ < λ . Setting a = 2 and e = 2 > 0, the new spectral information that is provided by e 0 jaj 0 coupling is expressed in terms of the ratios jaj (if λλ > 0) or e (if λλ < 0) and of the product jaje. The information is delivered in geometric form, both metric and trigonometric, associ- ated with various right-angled triangles deriving from optimality conditions on n−dimensional vectors. The paper contains a generalisation to indefinite matrices over R or C of Gustafson's operator trigonometry which in the matrix case assumes definiteness (mostly over R). Keywords: Spectral coupling, spectral chaining, hermitian matrix, indefinite symmetric or hermitian, spectral plane, observation angle, perspective angle, catchvector, antieigenvector, middle vector, Euler equation, balance equation, torus in 3D. 1. Composite natural phenomena 1.1. Introduction. It is well-known that many natural phenomena are composite: they are produced by the interaction of two (or more) simpler phenomena, where the interaction is in general nonlinear.
    [Show full text]